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better demo --------- Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: Taozhi Wang <taozhi.mark.wang@gmail.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> Co-authored-by: cyncyw <47289405+taozhiwang@users.noreply.github.com>
75 lines
2.7 KiB
Python
75 lines
2.7 KiB
Python
from pathlib import Path
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from rdagent.components.coder.model_coder.model import ModelExperiment
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from rdagent.core.prompts import Prompts
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from rdagent.core.scenario import Scenario
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prompt_dict = Prompts(file_path=Path(__file__).parent / "prompts.yaml")
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class GeneralModelScenario(Scenario):
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@property
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def background(self) -> str:
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return prompt_dict["general_model_background"]
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@property
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def source_data(self) -> str:
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raise NotImplementedError("source_data of GeneralModelScenario is not implemented")
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@property
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def output_format(self) -> str:
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return prompt_dict["general_model_output_format"]
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@property
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def interface(self) -> str:
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return prompt_dict["general_model_interface"]
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@property
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def simulator(self) -> str:
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return prompt_dict["general_model_simulator"]
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@property
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def rich_style_description(self) -> str:
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return """
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# General Model Scenario
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## Overview
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This demo automates the extraction and iterative development of models from academic papers, ensuring functionality and correctness.
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### Scenario: Auto-Developing Model Code from Academic Papers
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#### Overview
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This scenario automates the development of PyTorch models by reading academic papers or other sources. It supports various data types, including tabular, time-series, and graph data. The primary workflow involves two main components: the Reader and the Coder.
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#### Workflow Components
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1. **Reader**
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- Parses and extracts relevant model information from academic papers or sources, including architectures, parameters, and implementation details.
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- Uses Large Language Models to convert content into a structured format for the Coder.
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2. **Evolving Coder**
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- Translates structured information from the Reader into executable PyTorch code.
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- Utilizes an evolving coding mechanism to ensure correct tensor shapes, verified with sample input tensors.
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- Iteratively refines the code to align with source material specifications.
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#### Supported Data Types
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- **Tabular Data:** Structured data with rows and columns, such as spreadsheets or databases.
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- **Time-Series Data:** Sequential data points indexed in time order, useful for forecasting and temporal pattern recognition.
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- **Graph Data:** Data structured as nodes and edges, suitable for network analysis and relational tasks.
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"""
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def get_scenario_all_desc(self) -> str:
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return f"""Background of the scenario:
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{self.background}
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The interface you should follow to write the runnable code:
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{self.interface}
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The output of your code should be in the format:
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{self.output_format}
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The simulator user can use to test your model:
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{self.simulator}
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"""
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